Notes:
Notes:
library(ggplot2)
pf <- read.csv('pseudo_facebook.tsv', sep='\t')
qplot(age, friend_count, data = pf)
Response:
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) + geom_point()
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_jitter(alpha = 1/20) +
xlim(13, 90)
## Warning: Removed 5199 rows containing missing values (geom_point).
Response:
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_point(alpha = 1/20) +
xlim(13, 90) +
coord_trans(y = "sqrt")
## Warning: Removed 4906 rows containing missing values (geom_point).
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_point(alpha = 1/20) +
xlim(13, 90) +
coord_trans(y = "sqrt")
## Warning: Removed 4906 rows containing missing values (geom_point).
Notes:
ggplot(aes(x = age, y = friendships_initiated), data = pf) +
geom_jitter(alpha = 1/10) +
xlim(13, 90)
## Warning: Removed 5185 rows containing missing values (geom_point).
Notes:
Notes:
library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
age_groups <- group_by(pf, age)
pf.fc_by_age <- summarize(age_groups,
friend_count_mean = mean(friend_count),
friend_count_median = median(friend_count),
n = n())
pf.fc_by_age <- arrange(pf.fc_by_age, age)
head(pf.fc_by_age)
## Source: local data frame [6 x 4]
##
## age friend_count_mean friend_count_median n
## 1 13 164.7500 74.0 484
## 2 14 251.3901 132.0 1925
## 3 15 347.6921 161.0 2618
## 4 16 351.9371 171.5 3086
## 5 17 350.3006 156.0 3283
## 6 18 331.1663 162.0 5196
library(dplyr)
Create your plot!
library(ggplot2)
pf <- read.csv('pseudo_facebook.tsv', sep='\t')
names(pf.fc_by_age)
## [1] "age" "friend_count_mean" "friend_count_median"
## [4] "n"
ggplot(aes(age, friend_count_mean), data = pf.fc_by_age) + geom_line()
Notes:
ggplot(aes(x = age, y=friend_count), data = pf) +
geom_point(alpha=0.05,
position= position_jitter(h = 0),
color = "orange") +
coord_trans(y = 'sqrt') +
geom_line(stat = "summary", fun.y = mean)+
geom_line(stat="summary", fun.y = quantile, probs = 0.1,
linetype =2, color="blue") +
geom_line(stat="summary", fun.y = quantile, probs = 0.5,
linetype =2, color="blue") +
geom_line(stat="summary", fun.y = quantile, probs = 0.9,
linetype =2, color="blue")
Response:
See the Instructor Notes of this video to download Moira’s paper on perceived audience size and to see the final plot.
Notes:
Notes:
cor.test(pf$friend_count, pf$age)
##
## Pearson's product-moment correlation
##
## data: pf$friend_count and pf$age
## t = -8.6268, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03363072 -0.02118189
## sample estimates:
## cor
## -0.02740737
with(pf, cor.test(friend_count, age))
##
## Pearson's product-moment correlation
##
## data: friend_count and age
## t = -8.6268, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03363072 -0.02118189
## sample estimates:
## cor
## -0.02740737
Look up the documentation for the cor.test function.
What’s the correlation between age and friend count? Round to three decimal places. Response:
Notes:
?subset
with(subset(pf, age <= 70) , cor.test(age, friend_count))
##
## Pearson's product-moment correlation
##
## data: age and friend_count
## t = -52.5923, df = 91029, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1780220 -0.1654129
## sample estimates:
## cor
## -0.1717245
Notes:
Notes:
ggplot(aes(x=www_likes_received, y=likes_received), data = pf) +
geom_point()
Notes:
ggplot(aes(x=www_likes_received, y=likes_received), data=pf) +
geom_point() +
xlim(0,quantile(pf$www_likes_received, 0.95)) +
ylim(0,quantile(pf$likes_received, 0.95)) +
geom_smooth(method="lm", color="red")
## Warning: Removed 6075 rows containing missing values (stat_smooth).
## Warning: Removed 6075 rows containing missing values (geom_point).
What’s the correlation betwen the two variables? Include the top 5% of values for the variable in the calculation and round to 3 decimal places.
cor.test(pf$www_likes_received,pf$likes_received)
##
## Pearson's product-moment correlation
##
## data: pf$www_likes_received and pf$likes_received
## t = 937.1035, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9473553 0.9486176
## sample estimates:
## cor
## 0.9479902
Response:
Notes:
Notes:
#install.packages('alr3')
library(alr3)
## Loading required package: car
data(Mitchell)
names(Mitchell)
## [1] "Month" "Temp"
Create your plot!
ggplot(aes(x=Month, y=Temp), data=Mitchell) +
geom_point()
Take a guess for the correlation coefficient for the scatterplot.
What is the actual correlation of the two variables? (Round to the thousandths place)
cor.test(Mitchell$Month, Mitchell$Temp)
##
## Pearson's product-moment correlation
##
## data: Mitchell$Month and Mitchell$Temp
## t = 0.8182, df = 202, p-value = 0.4142
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08053637 0.19331562
## sample estimates:
## cor
## 0.05747063
Notes:
ggplot(aes(x=(Month%%12), y=Temp), data=Mitchell)+
geom_point()
ggplot(aes(x=Month, y=Temp), data=Mitchell) +
geom_point() +
scale_x_discrete(breaks = seq(0, 203, 12))
What do you notice? Response:
Watch the solution video and check out the Instructor Notes! Notes:
Notes:
pf$age_with_months <- pf$age + (1.0 - pf$dob_month/12)
names(pf)
## [1] "userid" "age"
## [3] "dob_day" "dob_year"
## [5] "dob_month" "gender"
## [7] "tenure" "friend_count"
## [9] "friendships_initiated" "likes"
## [11] "likes_received" "mobile_likes"
## [13] "mobile_likes_received" "www_likes"
## [15] "www_likes_received" "age_with_months"
head(pf$age_with_months)
## [1] 14.08333 14.08333 14.08333 14.00000 14.00000 14.00000
age_months_groups <- group_by(pf, age_with_months)
pf.fc_by_age_months <- summarise(age_months_groups,
friend_count_mean=mean(friend_count),
friend_count_median=median(friend_count),
n=n())
pf.fc_by_age_months <- arrange(pf.fc_by_age_months, age_with_months)
head(pf.fc_by_age_months)
## Source: local data frame [6 x 4]
##
## age_with_months friend_count_mean friend_count_median n
## 1 13.16667 46.33333 30.5 6
## 2 13.25000 115.07143 23.5 14
## 3 13.33333 136.20000 44.0 25
## 4 13.41667 164.24242 72.0 33
## 5 13.50000 131.17778 66.0 45
## 6 13.58333 156.81481 64.0 54
Programming Assignment
ggplot(aes(y=friend_count_mean, x=age_with_months),
data = subset(pf.fc_by_age_months, age_with_months <= 71)) +
geom_line()
ggplot(aes(y=friend_count_mean,x=age_with_months),
data=filter(pf.fc_by_age_months, age_with_months<=71))+
geom_line()
Notes:
p1 <- ggplot(aes(age,friend_count_mean), data =subset(pf.fc_by_age, age<=71)) +
geom_line()+
geom_smooth()
p2 <- ggplot(aes(y=friend_count_mean,x=age_with_months),
data=filter(pf.fc_by_age_months, age_with_months<=71))+
geom_line()+
geom_smooth()
p3 <- ggplot(aes(y= friend_count, x = round(age/5)*5),
data=subset(pf, age<= 71))+
geom_line(stat="summary", fun.y = mean)
library(gridExtra)
## Loading required package: grid
grid.arrange(p1,p2,p3,ncol=1)
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
Notes:
Reflection: We learn the comparison two variables in our data. We learned how figure out correlation between 2 variables. We learn how do visualization for analyze two variables. ***
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